Michael S. Pacella

3.2k total citations · 1 hit paper
16 papers, 1.4k citations indexed

About

Michael S. Pacella is a scholar working on Molecular Biology, Biomaterials and Biomedical Engineering. According to data from OpenAlex, Michael S. Pacella has authored 16 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 5 papers in Biomaterials and 4 papers in Biomedical Engineering. Recurrent topics in Michael S. Pacella's work include Calcium Carbonate Crystallization and Inhibition (5 papers), Advanced biosensing and bioanalysis techniques (5 papers) and RNA and protein synthesis mechanisms (4 papers). Michael S. Pacella is often cited by papers focused on Calcium Carbonate Crystallization and Inhibition (5 papers), Advanced biosensing and bioanalysis techniques (5 papers) and RNA and protein synthesis mechanisms (4 papers). Michael S. Pacella collaborates with scholars based in United States, Canada and Germany. Michael S. Pacella's co-authors include Jeffrey J. Gray, Jason W. Labonte, Richard Bonneau, Andrew Leaver‐Fay, Philip Bradley, Rhiju Das, P. Douglas Renfrew, Brian Kuhlman, Roland L. Dunbrack and Frank DiMaio and has published in prestigious journals such as Nature Communications, ACS Nano and PLoS ONE.

In The Last Decade

Michael S. Pacella

15 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

2017 942 citations Rebecca F. Alford, Andrew Leaver‐Fay et al. Journal of Chemical Theory and Computation profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Michael S. Pacella United States	11	927	315	218	150	150	16	1.4k
Gevorg Grigoryan United States	22	1.7k 1.8×	517 1.6×	247 1.1×	212 1.4×	118 0.8×	54	2.1k
Sylwia Rodziewicz‐Motowidło Poland	21	924 1.0×	229 0.7×	175 0.8×	70 0.5×	128 0.9×	109	1.6k
Nobuyasu Koga Japan	17	2.0k 2.1×	856 2.7×	161 0.7×	188 1.3×	193 1.3×	28	2.5k
Seung-Gu Kang United States	23	736 0.8×	667 2.1×	326 1.5×	64 0.4×	418 2.8×	58	1.7k
Patrick Fuchs France	26	1.6k 1.7×	203 0.6×	115 0.5×	265 1.8×	155 1.0×	61	2.4k
Jolene L. Lau United States	9	1.3k 1.4×	117 0.4×	231 1.1×	228 1.5×	139 0.9×	9	1.9k
Tudor Arvinte Switzerland	26	1.5k 1.6×	176 0.6×	278 1.3×	394 2.6×	269 1.8×	60	2.1k
Nelly R. Hajizadeh Germany	7	1.3k 1.4×	528 1.7×	82 0.4×	67 0.4×	89 0.6×	10	1.9k
Nicholas C. Fitzkee United States	24	1.1k 1.2×	602 1.9×	254 1.2×	52 0.3×	223 1.5×	59	1.7k
Scott E. Boyken United States	20	2.3k 2.4×	445 1.4×	202 0.9×	269 1.8×	344 2.3×	28	3.0k

Countries citing papers authored by Michael S. Pacella

Since Specialization

Citations

This map shows the geographic impact of Michael S. Pacella's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael S. Pacella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael S. Pacella more than expected).

Fields of papers citing papers by Michael S. Pacella

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael S. Pacella. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael S. Pacella. The network helps show where Michael S. Pacella may publish in the future.

Co-authorship network of co-authors of Michael S. Pacella

This figure shows the co-authorship network connecting the top 25 collaborators of Michael S. Pacella. A scholar is included among the top collaborators of Michael S. Pacella based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael S. Pacella. Michael S. Pacella is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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16 of 16 papers shown

Pacella, Michael S., et al.. (2024). Hierarchical assembly and modeling of DNA nanotube networks using Y-shaped DNA origami seeds. Nanoscale. 16(24). 11688–11695. 2 indexed citations

Jia, Sisi, Siew Cheng Phua, Yuta Nihongaki, et al.. (2021). Growth and site-specific organization of micron-scale biomolecular devices on living mammalian cells. arXiv (Cornell University). 14 indexed citations

Schaffter, Samuel W., Deepak K. Agrawal, Michael S. Pacella, et al.. (2020). Reconfiguring DNA Nanotube Architectures via Selective Regulation of Terminating Structures. ACS Nano. 14(10). 13451–13462. 17 indexed citations

Pacella, Michael S., et al.. (2020). A Student Perspective On Freshman Engineering Design Projects: Developing Core Skills In Young Engineers. 15.93.1–15.93.11.

Pacella, Michael S., et al.. (2019). Characterizing the length-dependence of DNA nanotube end-to-end joining rates. Molecular Systems Design & Engineering. 5(2). 544–558. 5 indexed citations

Pacella, Michael S., et al.. (2019). Characterizing DNA Nanotube Networks Assembled via Y-Junction DNA Origami Seeds. Biophysical Journal. 116(3). 273a–273a. 1 indexed citations

Pacella, Michael S., et al.. (2018). Chiral switching in biomineral suprastructures induced by homochiral l -amino acid. Science Advances. 4(8). eaas9819–eaas9819. 49 indexed citations

Athanasiadou, Dimitra, Dina Goldbaum, Kaustuv Basu, et al.. (2018). Nanostructure, osteopontin, and mechanical properties of calcitic avian eggshell. Science Advances. 4(3). eaar3219–eaar3219. 97 indexed citations

Lubin, Joseph H., Michael S. Pacella, & Jeffrey J. Gray. (2018). A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces. Langmuir. 34(18). 5279–5289. 2 indexed citations

10.

Pacella, Michael S. & Jeffrey J. Gray. (2017). A Benchmarking Study of Peptide–Biomineral Interactions. Crystal Growth & Design. 18(2). 607–616. 13 indexed citations

11.

Alford, Rebecca F., Andrew Leaver‐Fay, Jeliazko R. Jeliazkov, et al.. (2017). The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design. Journal of Chemical Theory and Computation. 13(6). 3031–3048. 942 indexed citations breakdown →

12.

Pacella, Michael S., Dimitra Athanasiadou, Valentin Nelea, et al.. (2017). Chiral acidic amino acids induce chiral hierarchical structure in calcium carbonate. Nature Communications. 8(1). 15066–15066. 160 indexed citations

13.

Pacella, Michael S., et al.. (2013). Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces. Methods in enzymology on CD-ROM/Methods in enzymology. 532. 343–366. 24 indexed citations

14.

Drew, Kevin, P. Douglas Renfrew, Timothy W. Craven, et al.. (2013). Adding Diverse Noncanonical Backbones to Rosetta: Enabling Peptidomimetic Design. PLoS ONE. 8(7). e67051–e67051. 50 indexed citations

15.

Kilambi, Krishna Praneeth, Michael S. Pacella, Jianqing Xu, et al.. (2013). Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20–27. Proteins Structure Function and Bioinformatics. 81(12). 2201–2209. 17 indexed citations

16.

Mitrano, Peter, et al.. (2011). Instabilities in the homogeneous cooling of a granular gas: A quantitative assessment of kinetic-theory predictions. Physics of Fluids. 23(9). 36 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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